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pro vyhledávání: '"Liu Jun S"'
It is increasingly recognized that participation bias can pose problems for genetic studies. Recently, to overcome the challenge that genetic information of non-participants is unavailable, it is shown that by comparing the IBD (identity by descent)
Externí odkaz:
http://arxiv.org/abs/2405.19058
A univariate continuous function can always be decomposed as the sum of a non-increasing function and a non-decreasing one. Based on this property, we propose a non-parametric regression method that combines two spline-fitted monotone curves. We demo
Externí odkaz:
http://arxiv.org/abs/2401.06383
We introduce a class of generic spike-and-slab priors for high-dimensional linear regression with grouped variables and present a Coordinate-ascent Variational Inference (CAVI) algorithm for obtaining an optimal variational Bayes approximation. Using
Externí odkaz:
http://arxiv.org/abs/2309.16855
Autor:
Lee, Taehee, Liu, Jun S.
Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. A
Externí odkaz:
http://arxiv.org/abs/2308.15370
In this paper, we prove that functional sliced inverse regression (FSIR) achieves the optimal (minimax) rate for estimating the central space in functional sufficient dimension reduction problems. First, we provide a concentration inequality for the
Externí odkaz:
http://arxiv.org/abs/2307.02777
We present a method for fitting monotone curves using cubic B-splines, which is equivalent to putting a monotonicity constraint on the coefficients. We explore different ways of enforcing this constraint and analyze their theoretical and empirical pr
Externí odkaz:
http://arxiv.org/abs/2307.01748
The varying coefficient model has received broad attention from researchers as it is a powerful dimension reduction tool for non-parametric modeling. Most existing varying coefficient models fitted with polynomial spline assume equidistant knots and
Externí odkaz:
http://arxiv.org/abs/2201.10063
Autor:
Yang, Xiaodong, Liu, Jun S.
This article is a discussion of Zanella and Roberts' paper: Multilevel linear models, gibbs samplers and multigrid decompositions. We consider several extensions in which the multigrid decomposition would bring us interesting insights, including vect
Externí odkaz:
http://arxiv.org/abs/2112.08641
Autor:
Yang, Xiaodong, Liu, Jun S.
The Multiple-try Metropolis (MTM) method is an interesting extension of the classical Metropolis-Hastings algorithm. However, theoretical understandings of its convergence behavior as well as whether and how it may help are still unknown. This paper
Externí odkaz:
http://arxiv.org/abs/2111.15084